Nanoparticles could make a reliable blood test for Alzheimer's disease a reality; image credit: National Cancer Institute, Daniel Sone Using nanoparticles with different surface properties, researchers are able to detect subtle changes in the composition of proteins in the plasma years before the presentation of clinical symptoms of Alzheimer's disease, which include memory loss, confusion, and cognitive difficulties. Owing to the unique properties of nanoparticles, different proteins in biological fluids selectively stick onto their surface forming a protein corona, which was found to change during disease. Researchers from the United States and Italy identify these subtle changes in plasma protein patterns to distinguish plasma samples from healthy individuals and those diagnosed with Alzheimer's disease. "Protein corona composition is both influenced by specific health conditions as well as the chemical and physical properties of the nanoparticles themselves," says Dr. Claudia Corbo of the University of Milano-Bicocca and lead author of the study published in Advanced Healthcare Materials. "Binding of proteins to the surface of particles is very precise and dependent on the chemistry and shape of the particles and the chemistry and structure of the proteins," says senior author Professor Omid Farokhzad of Brigham and Women's Hospital and Harvard Medical School.
Deductive formalisms have been strongly developed in recent years; among them, Answer Set Programming (ASP) gained some momentum, and has been lately fruitfully employed in many real-world scenarios. Nonetheless, in spite of a large number of success stories in relevant application areas, and even in industrial contexts, deductive reasoning cannot be considered the ultimate, comprehensive solution to AI; indeed, in several contexts, other approaches result to be more useful. Typical Bioinformatics tasks, for instance classification, are currently carried out mostly by Machine Learning (ML) based solutions. In this paper, we focus on the relatively new problem of analyzing the evolution of neurological disorders. In this context, ML approaches already demonstrated to be a viable solution for classification tasks; here, we show how ASP can play a relevant role in the brain evolution simulation task. In particular, we propose a general and extensible framework to support physicians and researchers at understanding the complex mechanisms underlying neurological disorders. The framework relies on a combined use of ML and ASP, and is general enough to be applied in several other application scenarios, which are outlined in the paper.
Artificial intelligence can be trained to spot structural changes in the brain linked to Alzheimer's disease nearly 10 years before doctors can diagnose it through symptoms, researchers claim. According to New Scientist, a team at the University of Bari in Italy has developed a machine learning algorithm that is able to spot alterations in how different regions of the brain are connected – alterations that could be early signs of the disease. Their algorithm was trained using MRI scans from 67 patients, 38 of which were from people affected by the disease and 29 from healthy patients. The scans came from the Alzheimer's Disease Neuroimaging Initiative database at the University of Southern California in Los Angeles. The AI was trained to correctly spot the difference between diseased and healthy brains, before being tested on its accuracy abilities on a second set of 148 scans – 52 of which were healthy, 48 had Alzheimer's and the other 48 had a mild cognitive impairment that was known to develop into Alzheimer's within 10 years.
ARTIFICIAL Intelligence could be used to pick up Alzheimer's ten years before symptoms surface, scientists have claimed. AI algorithms have been successfully tested in pinpointing healthy brains and those with the disease with 86 per cent accuracy, leading to hopes it could ultimately be used by the NHS to predict Alzheimer's. It is further hoped that the diagnosis tool could be used privately within a decade, according to The Times. The breakthrough, made by Marianna La Rocca, of the University of Bari in Italy, could mean the onset of symptoms could be delayed. La Rocca's algorithm was tested on 38 scans of patients with Alzheimer's and 29 of those without the disease, with it then tested on another 148 people.
Artificial intelligence (AI) can identify Alzheimer's disease 10 years before doctors can discover the symptoms, according to new research. A team of researchers in Italy developed an algorithm that can spot structural changes in the brain that are caused by the disease a decade before the signs become apparent. The team from the University of Bari trained the AI by feeding in 67 MRI scans - 38 from Alzheimer's patients and 29 healthy patients - then asked it to analyse the neuronal connectivity to form an algorithm. Following the training, the AI was then asked to process brains from 148 subjects - 52 were healthy, 48 had Alzheimer's disease and 48 had mild cognitive impairment (MCI) but were known to have developed Alzheimer's disease two and a half to nine years later. According to the researchers, the AI diagnosed Alzheimer's disease 86 per cent of the time.
Various researchers around the globe are developing ways to detect Alzheimer's as early as possible. After all, early detection gives people the power seek treatment that can slow down the condition's effects, as well as enough time to get their legal and financial affairs in order. Some decided to focus on blood and cerebrospinal fluid tests, while others are developing gadgets that can look for early signs. A team of researchers from the University of Bari in Italy, however, believe the answer lies in artificial intelligence. They trained their AI by feeding it 67 MRI scans -- 38 from Alzheimer's patients and 29 from healthy controls.
Early diagnosis can help patients make lifestyle changes which may help slow the progression of Alzheimer's. A devastating chronic neurodegenerative disease, Alzheimer's disease (AD) currently affects around 5.5 million people in the United States alone. Causing progressive mental deterioration, it ultimately advances to impact basic bodily functions such as walking and swallowing. Looking for a way to help, researchers at the University of Bari and Istituto Nazionale di Fisica Nucleare in Italy have developed new machine learning AI technology that may help identify Alzheimer's a decade before doctors usually can, by way of non-invasive MRI brain scans. An early diagnosis -- before any of the symptoms a doctor might recognize become apparent -- could give patients a chance to make changes to their lifestyle which may slow Alzheimer's progression.